First principles · before the mechanics
A university is not a company. Its AI governance shouldn't pretend to be.
Why higher education is different
Corporate AI governance optimizes for the shareholder. A university answers to students it holds in trust, to a mission of truth-seeking, and to shared governance and academic freedom. Those commitments change what "responsible AI" means here — and generic frameworks ignore them.
Students are a vulnerable population
They can't opt out of the systems that grade, flag, and route them. The institution holds a duty of care a customer relationship never carries.
Truth-seeking is the product
AI that fabricates, flatters, or erodes critical thinking attacks the university's core purpose — not just its efficiency.
Authority is shared, not top-down
Faculty governance and academic freedom mean policy must be legitimate, not just issued. Governance that ignores this won't hold.
Why this matters
Principles are the constant the mechanics serve. When a domain, tier, or vendor contract raises a hard call, these six commitments are what you decide against — and what makes the decision defensible to a board, a faculty senate, or a student.
Six durable commitments
01
Human judgment owns consequential decisions
AI can inform, draft, rank, and flag — but a person remains accountable for any decision that materially affects a student, an employee, or the institution. Automation is never the author of a consequential outcome.
02
The affected have a right to know and to contest
Anyone materially affected by an AI-influenced decision is entitled to notice that AI was used, a plain-language explanation, and a meaningful path to appeal to a human.
03
Oversight is proportional to stakes
Rigor scales with consequence. Low-stakes tools move fast; high-stakes uses earn real scrutiny. Proportionality is what keeps governance from becoming either theater or bureaucracy.
04
Academic freedom and inquiry are protected
Governance constrains institutional risk, not scholarly freedom. Faculty and researchers retain latitude to study, critique, and experiment with AI — including its failures.
05
Equity is a design requirement, not an audit finding
Disparate impact is anticipated and tested for before deployment, not discovered afterward. The burden is on the system to prove it is fair, not on the harmed to prove it is not.
06
Transparency is the default; opacity must be justified
The institution documents where AI operates, what it decides, and on what basis. Secrecy about AI use is the exception that requires a reason — not the norm.